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Paveo
Stop risky AI agent actions before they run
Let your AI agents act, and prove what you allowed. When your coding agent tries to run rm -rf or force-push to main, Paveo refuses it before it runs and records the decision. Works with Claude Code, Codex, Cursor, MCP and Python agents, on your own machine. With design partners: a model trained on your team's own decisions that flags what the rules allow but shouldn't, and someone on your team to approve what gets flagged. Try it: uvx paveo doctor
Hi everyone, I'm Faiz, and I build Paveo.
It started with a hook. I wrote a Claude Code hook to stop rm -rf, then found it would let the command through if the hook itself crashed. So I looked at public repos: searching GitHub for hooks that read certain environment variables, 86 of 153 settings files had at least one hook that can't work as written, mostly because it reads a variable Claude Code doesn't set. ZenML and an AWS samples repo have since merged fixes.
Paveo is the checkpoint I wanted instead. Shell commands, file edits and tool calls are checked against your policy before they run. A refused call never happens, the agent is told why, and every decision lands in a tamper-evident log. It runs on your machine and sends nothing anywhere.
Three ways in:
- uvx paveo doctor checks the Claude Code hooks you already have, with nothing to set up
- paveo init claude-code (or codex, cursor) installs the guard
- paveo replay claude-code shows what it would have refused in your past sessions, before you turn it on. On mine: 173 rm -rf across 162 sessions.
What it isn't: a sandbox. It reads commands as text, so a disguised command can get past it, and the README says exactly where.
Where it's going: rules can't capture every judgment call. With our design partners we're building a model trained on your team's own decisions that flags what the rules allow but shouldn't, and someone on your team to approve what gets flagged. It can only make a decision stricter, never looser, so the proof stays intact. We're onboarding a small group now. If that's you, reply here or write to faiz@trypaveo.com.
One question I'd love answers to: what would your security team need to see before letting an agent act in production?
About Paveo on Product Hunt
“Stop risky AI agent actions before they run”
Paveo was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #120 on the daily leaderboard. Let your AI agents act, and prove what you allowed. When your coding agent tries to run rm -rf or force-push to main, Paveo refuses it before it runs and records the decision. Works with Claude Code, Codex, Cursor, MCP and Python agents, on your own machine. With design partners: a model trained on your team's own decisions that flags what the rules allow but shouldn't, and someone on your team to approve what gets flagged. Try it: uvx paveo doctor
On the analytics side, Paveo competes within Developer Tools, Artificial Intelligence, GitHub and Security — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Paveo performed against the three products that launched closest to it on the same day.
Who hunted Paveo?
Paveo was hunted by MD Faiz Jamal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Paveo including community comment highlights and product details, visit the product overview.
MD Faiz Jamal